Titlebook: Biometric Recognition; 13th Chinese Confere Jie Zhou,Yunhong Wang,Zhenhua Guo Conference proceedings 2018 Springer Nature Switzerland AG 20

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书目名称Biometric Recognition影响因子(影响力)




书目名称Biometric Recognition影响因子(影响力)学科排名




书目名称Biometric Recognition网络公开度




书目名称Biometric Recognition网络公开度学科排名




书目名称Biometric Recognition被引频次




书目名称Biometric Recognition被引频次学科排名




书目名称Biometric Recognition年度引用




书目名称Biometric Recognition年度引用学科排名




书目名称Biometric Recognition读者反馈




书目名称Biometric Recognition读者反馈学科排名

FLIP

nugatory

敌手

Reference work 2017Latest editionherefore significant for finger-vein recognition improvement. In this paper, we propose a novel feature encoding method based on local graph structure (LGS), which behaves better in improving the matching accuracy of features. In terms of the variations of veins in running direction, oriented Gabor

FEAT

Teresa L. McCarty,Serafín M. Coronel-Molinay to achieve high recognition accuracy. To extract pores accurately and robustly, we propose a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation. More specifically, pore candidates are coarsely estimated using logical operation at first; then, c

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包租车船

Hemiplegia

Anna Solin,Hanna-Mari Pienimäkised. However, most of the existing image acquisition systems are based on commercial equipment which has high cost, big equipment volume, over-high precision and long 3D data generation time. What’s more, those systems are not designed specialized for palmprint. Most of their parameters are not suit

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